Method and system to dynamically enable SDN network learning capability in a user-defined cloud network

ABSTRACT

A software defined network controller for controlling a radio access network has a leaning capability by which usage characteristics are learned for each service provided by the radio access network. For example, bandwidth data, connection duration data and latency data may be used to learn usage characteristics for each radio access network service. The learned usage characteristics are then used in allocating network resources in response to a service request.

TECHNICAL FIELD

Embodiments of the present disclosure relate to user-defined cloudnetworks. Specifically, the disclosure relates to the use of an enhancedsoftware-defined-network controller to improve utilization of radioaccess network resources.

BACKGROUND

The software defined network (SDN) concept allows a separation of thecontrol plane from the user or application plane in a communicationsnetwork. The concept allows the management of network services throughabstraction of higher-level functionality. An SDN controller is alogically centralized entity that performs several operations. Thoseoperations include translating the requirements from the SDN applicationlayer down to the SDN datapath or infrastructure layer. The operationsfurther comprise providing the SDN applications with an abstract view ofthe network (which may include statistics and events). An SDN controllercan be leveraged for many enhanced capabilities.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure can be readily understood by considering thefollowing detailed description in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a block diagram showing a communications network in accordancewith aspects of the present disclosure.

FIG. 2 is a block diagram showing a virtualized communications networkin accordance with aspects of the present disclosure.

FIG. 3 is a block diagram showing a system in accordance with aspects ofthe present disclosure.

FIG. 4 is a flow diagram illustrating operations in accordance withaspects of the present disclosure.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

In the presently described application, the SDN controller is enhancedwith network learning capability to efficiently and intelligentlyutilize network resources in a radio access network (RAN). To that end,the SDN controller may incorporate learning capabilities that include,for example, how much bandwidth a streaming video requires for aparticular service, what are the other plays into latency andefficiency, and what is the traffic pattern and trend of streaming inthe network for that particular service. Intelligent data collection,either from the network or from user devices, is performed and adatabase is maintained either at the RAN SDN controller level or in theRAN data plane application level that can feed back to the SDNcontroller. The SDN controller can leverage the learned network andservice behavior for more intelligent network control of the RAN.

Embodiments of the present disclosure include a software defined networkcontroller having an interface with a database store with radio accessnetwork usage data for each of a plurality of radio access networkservices provided on a radio access network. The controller includes anintelligent data collection function configured for collecting the radioaccess network usage data from a core network of the radio accessnetwork on a per-flow basis for each of the plurality of radio accessnetwork services, and for storing the radio access network usage data inthe database store.

The SDN controller further includes a network resource allocation enginecomprising an interface with the database store, a processor and atleast one memory device storing computer readable instructions that,when executed by the processor, cause the processor to executeoperations comprising: for each of the plurality of radio access networkservices, applying a learning algorithm to determine learned networkusage requirements based on the radio access network usage data; andallocating resources per service of the radio access network based onthe learned usage requirements of each service and based on bandwidthand latency requirements computed from service requirements.

In further embodiments of the disclosure, a method for allocatingresources in a radio access network using a software defined networkcontroller comprises the following operations. Radio access networkusage data is collected from a core network of the radio access networkfor each of a plurality of radio access network services provided on theradio access network, the radio access network usage data being on aper-flow basis for each of the plurality of radio access networkservices. Bandwidth and latency requirements are computed from servicerequirements for each of the plurality of radio access network services.For each of the plurality of radio access network services, a learningalgorithm is applied to determine learned network usage requirementsbased on the radio access network usage data. Resources are thenallocated per service of the radio access network based on the learnedusage requirements of each service and based on the bandwidth andlatency requirements.

In further embodiments of the disclosure, a computer-readable storagedevice has stored thereon computer readable instructions for allocatingresources in a radio access network using a software defined networkcontroller. Execution of the computer readable instructions by aprocessor causes the processor to perform operations comprising:collecting, from a core network of the radio access network, radioaccess network usage data for each of a plurality of radio accessnetwork services provided on the radio access network, the radio accessnetwork usage data being on a per-flow basis for each of the pluralityof radio access network services; computing bandwidth and latencyrequirements from service requirements for each of the plurality ofradio access network services; for each of the plurality of radio accessnetwork services, applying a learning algorithm to determine learnednetwork usage requirements based on the radio access network usage data;and allocating resources per service of the radio access network basedon the learned usage requirements of each service and based on thebandwidth and latency requirements.

The presently disclosed concepts may be implemented to improveefficiencies, capacity and user experience of a radio access networkwithin a communications network environment. Such a communicationsnetwork in accordance with various aspects described herein isillustrated by the block diagram 100 shown in FIG. 1. In particular, acommunications network 125 is presented for providing broadband access110 to a plurality of data terminals 114 via access terminal 112,wireless access 120 to a plurality of mobile devices 124 and vehicle 126via base station or access point 122, voice access 130 to a plurality oftelephony devices 134, via switching device 132 and/or media access 140to a plurality of audio/video display devices 144 via media terminal142. In addition, communication network 125 is coupled to one or morecontent sources 175 of audio, video, graphics, text or other media.While broadband access 110, wireless access 120, voice access 130 andmedia access 140 are shown separately, one or more of these forms ofaccess can be combined to provide multiple access services to a singleclient device.

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched telephonenetwork, a voice over Internet protocol (VoIP) network, Internetprotocol (IP) based television network, a cable network, a passive oractive optical network, a 4G or higher wireless radio access network,WIMAX network, UltraWideband network, personal area network or otherwireless access network, a broadcast satellite network and/or othercommunications network.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) or other access terminal. Thedata terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G or higher modem, an optical modem and/or other accessdevices.

In various embodiments, the base station or access point 122 can includea 4G or higher base station in a radio access network, an access pointthat operates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and other sourcesof media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

Referring now to FIG. 2, a block diagram 200 illustrating an example,non-limiting embodiment of a virtualized communication network inaccordance with various aspects described herein, is shown. Inparticular, a virtualized communication network is presented that can beused to implement some or all of the communications network 125presented in conjunction with FIG. 1.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 250, virtualized network function cloud 225 and/or oneor more cloud computing environments 275. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs), reduces complexity fromservices and operations; supports more nimble business models andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements 230, 232, 234, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrate. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or merchant silicon arenot appropriate. In this case, communication services can be implementedas cloud-centric workloads.

As an example, a traditional network element 150, such as a radio accessnetwork element can be implemented via a virtual network element 234composed of NFV software modules, merchant silicon, and associatedcontrollers such as the software defined network controller 292. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it iselastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing that infrastructure easier to manage.

In an embodiment, the transport layer 250 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless radio access 120, voice access130, media access 140 and/or access to content sources 175 fordistribution of content to any or all of the access technologies. Inparticular, in some cases a network element needs to be positioned at aspecific place, and this allows for less sharing of commoninfrastructure. Other times, the network elements have specific physicallayer adapters that cannot be abstracted or virtualized, and mightrequire special DSP code and analog front-ends (AFEs) that do not lendthemselves to implementation as virtual network elements 230, 232 or234. These network elements can be included in transport layer 250. Forexample, a radio access network providing wireless access 120 maycomprise a plurality of cellular base stations 122 interconnected by abackhaul infrastructure. Those physical layer elements do not lendthemselves to abstraction and are therefore included in the transportlayer 250. They communicate with the SDN controller using an SDNprotocol such as the OpenFlow™ protocol.

The virtualized network function cloud 225 interfaces with the transportlayer 250 to provide the virtual network elements 230, 232, 234, etc. toprovide specific NFVs. In particular, the virtualized network functioncloud 225 leverages cloud operations, applications, and architectures tosupport networking workloads. The virtualized network elements 230, 232and 234 can employ network function software that provides either aone-for-one mapping of traditional network element function oralternately some combination of network functions designed for cloudcomputing. For example, virtualized network elements 230, 232 and 234can include route reflectors, domain name system (DNS) servers, anddynamic host configuration protocol (DHCP) servers, system architectureevolution (SAE) and/or mobility management entity (MME) gateways,broadband network gateways, IP edge routers for IP-VPN, Ethernet andother services, load balancers, distributers and other network elements.Because these elements don't typically need to forward large aggregatesof traffic, their workload can be distributed across a number ofservers—each of which adds a portion of the capability, and overallwhich creates an elastic function with higher availability than itsformer monolithic version. These virtual network elements 230, 232, 234,etc. can be instantiated and managed using an orchestration approachsimilar to those used in cloud compute services.

The cloud computing environments 275 can interface with the virtualizednetwork function cloud 225 via APIs that expose functional capabilitiesof the VNE 230, 232, 234, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 225. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 225 and cloud computingenvironment 275 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations. In one example, a control, orchestrator management andpolicy server 290 interfaces with the SDN controller 292 to overseeorchestration, control and policy management of the wireless radioaccess 120.

In the present disclosure, a network learning capability is implementedin the SDN controller 292 to efficiently and intelligently utilizenetwork resources in a RAN. Mobile traffic has been growing at a veryfast pace and the trend is continuing. In addition, the divergence ofradio technologies, types of devices, and service requirements are allwidening. For example, smart mobile devices requiring enhanced mobilebroadband have become ubiquitous in many areas of the world.Concurrently, the Internet of things (IoT) is expected to continue itsextreme growth. That growth will result in a massive number of sensornodes with low bandwidth and relaxed latency requirements. Othercommunications requiring ultra-reliability and low latency, such as theconnected car, have also grown rapidly.

To meet those divergent requirements, mobile service providers areactively looking for ways to improve network efficiency, system capacityand end user experience. The providers are developing new, moreefficient technologies, such as 5G. At the same time, network resourcesare being sliced for different types of services to allow all servicesto share the same network resources. Mobile service providers areadditionally attempting to select the most suitable radio technologiesfor a given service.

Many data collection mechanisms are available to network operators forcollecting data such as network load conditions. The present describesthe use of a learning capability for each RAN service, such as for eachstreaming video service (movies on demand, video telecommunications,etc.). That learning capability determines network characteristics ofeach service, such as the bandwidth a streaming video service requires,the other requirements the streaming video service has, such as latencyand preferred codec, and the traffic pattern and trend of the streamingin the network. Those network characteristics of each service are usedin proactively making network resource allocation decisions. Thatcapability allows a more dynamic network slicing decision to bestutilize the limited RAN resources.

The disclosed system and method provide enhancement to the SDNcontroller with network learning capability to better make controldecisions that efficiently utilize network resources in the RAN. Anexemplary arrangement illustrating the solution is now described withreference to the block diagram 300 of FIG. 3.

A control, orchestrator management and policy server 310 receives aservice request 305 and transmits associated service requirements 315 toa data analytics/RAN SDN controller 320 in a virtual network element325. The SDN controller converts the service requirements 315 tobandwidth/delay requirements. The data analytics/RAN SDN controller 320collects the per flow information regarding bandwidth, how long eachsession lasts, etc. The flows may each be associated with one of avariety of wireless service types in a RAN 370, such as a video flowstreaming from a video content server to smart phones 331, 332 viawireless base stations 334, 335, or an automatic meter reading serviceutilizing an IoT utility meter 333, or a WiFi™ network utilizing awireless access point 336. Data traffic flows in the RAN 370 take placein the transport layer using a backhaul network 340 coordinated usingOpenFlow™ servers 341. Data describing the individual flows may becollected via paths 350, 351, 352 from the base stations 334, 335, andaccess point 336. The data collection may utilize the backhaul network340 or may be independent of it. Similar data may also be collected fromthe devices (UEs) 331, 332, 333, which can potentially provide moreinsight on how the flows are consumed and what the user behaviors are inhandling the service/application.

The collected data may be stored in an SDN database 321 for futurereference and fed to the SDN controller 320, which can proactively makenetwork resource allocation decisions to best utilize the limited RANresources. For example, network slicing decisions may be made moredynamically based on increased knowledge of the requirements of each RANservice. The process continuously improves the SDN controller 320 bylearning about each service, including learning about the resourcesallocated for service, user behavior for each service and dynamicchanges performed for that service. That learning process helps thecontroller to recognize each service need and to allocate resourcesaccordingly.

A method for allocating resources in a radio access network using asoftware defined network controller, in accordance with embodiments ofthe disclosure, will now be discussed with reference to the blockdiagram 400 of FIG. 4. The method 400 may be performed by a softwaredefined network controller with interfaces for controlling a radioaccess network, and for collecting data from the radio access network.The radio access network services may include, for example, voicecommunications services, video content delivery services and videocommunications services.

Radio access network usage data is collected from a core network of theRAN at operation 410 for each of a plurality of radio access networkservices provided on the radio access network. The radio access networkusage data is on a per-flow basis for each of the plurality of radioaccess network services. The collected data may include bandwidth data,connection duration data and latency data for each of the radio accessnetwork services. In addition, user behavior data from user devices ofthe radio access network may be collected on a per-flow basis for eachof the plurality of radio access network services.

Bandwidth and latency requirements for each of the plurality of radioaccess network services are computed at operation 420 from servicerequirements for each of the plurality of RAN services. The servicerequirements may be received from a control, orchestration managementand policy server. The service requirements may be provided to the SDNcontroller upon receipt of an initial service request by the control,orchestration management and policy server, and provide for efficientcontrol, operation and management of the RAN. The service requirementsprovided by the control, orchestration management and policy server inresponse to future service requests may be adjusted over time as the SDNcontroller learns current characteristics of each of the RAN services.

For each of the plurality of radio access network services, a learningalgorithm is applied at operation 430 to determine learned network usagerequirements based on the radio access network usage data. The learningalgorithm may further determine learned network usage requirements basedon network usage changes resulting from previous resource allocations.

The resources per service of the radio access network are allocated atoperation 440 based on the learned usage requirements of each serviceand based on the bandwidth and latency requirements. Those resources mayinclude bandwidth per service of the radio access network.

The hardware and the various network elements used in implementing theabove-described technique comprise one or more processors, together withinput/output capability and computer readable storage devices havingcomputer readable instructions stored thereon that, when executed by theprocessors, cause the processors to perform various operations. Theprocessors may be dedicated processors, or may be mainframe computers,desktop or laptop computers or any other device or group of devicescapable of processing data. The processors are configured using softwareaccording to the present disclosure.

Each of the hardware elements also includes memory that functions as adata memory that stores data used during execution of programs in theprocessors, and is also used as a program work area. The memory may alsofunction as a program memory for storing a program executed in theprocessors. The program may reside on any tangible, non-volatilecomputer-readable storage device as computer readable instructionsstored thereon for execution by the processor to perform the operations.

Generally, the processors are configured with program modules thatinclude routines, objects, components, data structures and the like thatperform particular tasks or implement particular abstract data types.The term “program” as used herein may connote a single program module ormultiple program modules acting in concert. The disclosure may beimplemented on a variety of types of computers, including personalcomputers (PCs), hand-held devices, multi-processor systems,microprocessor-based programmable consumer electronics, network PCs,mini-computers, mainframe computers and the like, and may employ adistributed computing environment, where tasks are performed by remoteprocessing devices that are linked through a communications network. Ina distributed computing environment, modules may be located in bothlocal and remote memory storage devices.

An exemplary processing module for implementing the methodology abovemay be stored in a separate memory that is read into a main memory of aprocessor or a plurality of processors from a computer readable storagedevice such as a ROM or other type of hard magnetic drive, opticalstorage, tape or flash memory. In the case of a program stored in amemory media, execution of sequences of instructions in the modulecauses the processor to perform the process operations described herein.The embodiments of the present disclosure are not limited to anyspecific combination of hardware and software.

The term “computer-readable medium” as employed herein refers to atangible, non-transitory machine-encoded medium that provides orparticipates in providing instructions to one or more processors. Forexample, a computer-readable medium may be one or more optical ormagnetic memory disks, flash drives and cards, a read-only memory or arandom access memory such as a DRAM, which typically constitutes themain memory. The terms “tangible media” and “non-transitory media” eachexclude transitory signals such as propagated signals, which are nottangible and are not non-transitory. Cached information is considered tobe stored on a computer-readable medium. Common expedients ofcomputer-readable media are well-known in the art and need not bedescribed in detail here.

The presently disclosed technique implements an SDN learning capabilityin which the SDN controller continuously learns about each service andthe network resources allocated to each service flow and the dynamicchanges that must be made for that service. That learning processenables the controller to recognize each service need, to leverage thelearned network and service behavior for more intelligent networkcontrol and to allocate resources accordingly. The presently describedimprovements to a RAN SDN controller therefore provide a moreintelligent network control and allocation of radio resources, animprovement of network efficiency and the ability to meet a greatervariety of network service requirements.

The forgoing detailed description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the disclosure herein is not to be determined from the description,but rather from the claims as interpreted according to the full breadthpermitted by the patent laws. Also, it is to be understood that thephraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having” and variations thereof herein ismeant to encompass the items listed thereafter and equivalents thereofas well as additional items. Unless specified or limited otherwise, theterms “mounted,” “connected,” “supported,” and “coupled” and variationsthereof are used broadly and encompass direct and indirect mountings,connections, supports, and couplings. Further, “connected” and “coupled”are not restricted to physical or mechanical connections or couplings.It is to be understood that various modifications will be implemented bythose skilled in the art, without departing from the scope and spirit ofthe disclosure.

What is claimed is:
 1. A software defined network controller,comprising: a database store comprising bandwidth usage data, connectionduration usage data and latency usage data, wherein each of thebandwidth usage data, connection duration usage data and latency usagedata is collected via a path utilizing a backhaul network from userdevices of a radio access network on a per-flow basis for each of aplurality of radio access network services provided on the radio accessnetwork; a processor configured to perform an intelligent datacollection function including collecting the bandwidth usage data,connection duration usage data and latency usage data via the backhaulnetwork from the user devices of the radio access network on a per-flowbasis for each of the plurality of radio access network services, andstoring the bandwidth usage data, connection duration usage data andlatency usage data in the database store; and a network resourceallocation engine comprising an interface with the database store, aprocessor and at least one memory device storing computer readableinstructions that, when executed by the processor, cause the processorto execute operations comprising: receiving, from the intelligent datacollection function, the bandwidth usage data, connection duration usagedata and latency usage data; for each of the plurality of radio accessnetwork services, applying to the bandwidth usage data, connectionduration usage data and latency usage data, a learning algorithm todetermine, for each of the radio access network services individually,learned network characteristics of the radio access network service,including current trends in streaming and traffic patterns, bandwidthand latency requirements and preferred codec, and network usage changesresulting from previous resource allocations; receiving, from a control,orchestration management and policy server, service requirements for arequested radio access network service of the radio access networkservices; recognizing a service need for the requested radio accessnetwork service based on the learned network characteristics of theradio access network service; and allocating resources for the requestedradio access network service based on the learned networkcharacteristics of the requested radio access network service and basedon bandwidth and latency requirements computed from the servicerequirements.
 2. The software defined network controller of claim 1,wherein the radio access network services comprise at least voicecommunications services, video content delivery services and videocommunications services.
 3. The software defined network controller ofclaim 1, wherein the allocating resources comprises allocating bandwidthper service of the radio access network.
 4. A method for allocatingresources in a radio access network using a software defined networkcontroller, comprising: collecting, via a path utilizing a backhaulnetwork from user devices of the radio access network, bandwidth usagedata, connection duration usage data and latency usage data for each ofa plurality of radio access network services provided on the radioaccess network, the bandwidth usage data, connection duration usage dataand latency usage data being collected from the user devices of theradio access network on a per-flow basis for each of the plurality ofradio access network services; receiving, from a control, orchestrationmanagement and policy server, service requirements for a requested radioaccess network service of the radio access network services; computingbandwidth and latency requirements from the service requirements for therequested radio access network service; applying to the bandwidth usagedata, connection duration usage data and latency usage data for therequested radio access network service, a learning algorithm todetermine, for each of the radio access network services individually,learned network characteristics of the radio access network service,including current trends in streaming and traffic patterns, bandwidthand latency requirements and preferred codec, and network usage changesresulting from previous resource allocations; recognizing a service needfor the requested radio access network service based on the learnednetwork characteristics of the radio access network service; andallocating resources for the requested radio access network servicebased on the learned network characteristics of the requested radioaccess network service and based on the bandwidth and latencyrequirements.
 5. The method of claim 4, wherein the radio access networkservices comprise at least voice communications services, video contentdelivery services and video communications services.
 6. The method ofclaim 4, wherein the allocating resources comprises allocating bandwidthper service of the radio access network.
 7. A computer-readable storagedevice having stored thereon computer readable instructions forallocating resources in a radio access network using a software definednetwork controller, wherein execution of the computer readableinstructions by a processor causes the processor to perform operationscomprising: collecting, via a path utilizing a backhaul network fromuser devices of the radio access network, bandwidth usage data,connection duration usage data and latency usage data for each of aplurality of radio access network services provided on the radio accessnetwork, the bandwidth usage data, connection duration usage data andlatency usage data being collected from the user devices of the radioaccess network on a per-flow basis for each of the plurality of radioaccess network services; receiving, from a control, orchestrationmanagement and policy server, service requirements for a requested radioaccess network service of the radio access network services; computingbandwidth and latency requirements from the service requirements for therequested radio access network service; applying to the bandwidth usagedata, connection duration usage data and latency usage data for therequested radio access network service, a learning algorithm todetermine, for each of the radio access network services individually,learned network characteristics of the radio access network service,including current trends in streaming and traffic patterns, bandwidthand latency requirements and preferred codec, and network usage changesresulting from previous resource allocations; recognizing a service needfor the requested radio access network service based on the learnednetwork characteristics of the radio access network service; andallocating resources for the requested radio access network servicebased on the learned network characteristics of the requested radioaccess network service and based on the bandwidth and latencyrequirements.
 8. The computer-readable storage device of claim 7,wherein the radio access network services comprise at least voicecommunications services, video content delivery services and videocommunications services.